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Open Access Methodology article

Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

Li-Jen Su12, Ching-Wei Chang3, Yu-Chung Wu2, Kuang-Chi Chen4, Chien-Ju Lin1, Shu-Ching Liang1, Chi-Hung Lin5, Jacqueline Whang-Peng1, Shih-Lan Hsu6, Chen-Hsin Chen37* and Chi-Ying F Huang11089*

Author Affiliations

1 Institute of Cancer Research, National Health Research Institutes, Taipei 114, Taiwan

2 Department of Surgery, Veterans General Hospital, Taipei 112, Taiwan

3 Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan

4 Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan

5 Institute of Microbiology and Immunology, National Yang-Ming University, Taipei 112, Taiwan

6 Department of Education and Research, Taichung Veterans General Hospital, Taichung 407, Taiwan

7 Institute of Epidemiology, National Taiwan University, Taipei 100, Taiwan

8 Institute of Bio-Pharmaceutical Sciences, National Yang-Ming University, Taipei 112, Taiwan

9 Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan

10 Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

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BMC Genomics 2007, 8:140  doi:10.1186/1471-2164-8-140

Published: 1 June 2007



The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.


Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively.


Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.